Toward Generalized Detection of Synthetic Media: Limitations, Challenges, and the Path to Multimodal Solutions

Artificial intelligence (AI) in media has seen rapid advancements over the past decade, particularly with the introduction of Generative Adversarial Networks (GANs) and diffusion models, which have enhanced photorealistic image generation. However, these developments have also led to challenges in distinguishing between real and synthetic content, as evidenced by the rise of deepfakes. Many detection models utilizing deep learning methods like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have been created, but they often struggle with generalization and multimodal data.